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2000
Volume 32, Issue 12
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Introduction

The CLDN18 gene, encoding claudin 18.1 and claudin 18.2, is a key component of tight junction strands in epithelial cells that form a paracellular barrier that is critical in Stomach Adenocarcinoma (STAD).

Methods

Our study included 1,095 patients with proven STAD, 415 from The Cancer Genome Atlas (TCGA) cohort and 680 from the Gene Expression Omnibus database. We applied various analyses, including gene set enrichment analysis, pathway analysis, and drug screening to evaluate survival, immune cells, and genes and gene sets associated with cancer progression, based on CLDN18 expression levels. Gradient boosting machine learning (70% for training, 15% for validation, and 15% for testing) was used to evaluate the impact of CLDN18 on survival and develop a survival prediction model.

Results

High CLDN18 expression correlated with worse survival in lymphocyte-poor STAD, accompanied by decreased helper T cells, altered metabolic genes, low necrosis-related gene expression, and increased tumor proliferation. CLDN18 expression showed associations with gene sets associated with various stomach, breast, ovarian, and esophageal cancers, while pathway analysis linked CLDN18 to immunity. Incorporating CLDN18 expression improved survival prediction in a machine learning model. Notably, nutlin-3a and niraparib effectively inhibited high CLDN18-expressing gastric cancer cells in drug screening.

Conclusion

Our study provides a comprehensive understanding of the biological role of CLDN18-based bioinformatics and machine learning analysis in STAD, shedding light on its prognostic significance and potential therapeutic implications. To fully elucidate the molecular intricacies of CLDN18, further investigation is warranted, particularly through and studies.

© 2025 Bentham Science Publishers
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CLDN18: Clinical, Pathological, and Genetic Signatures with Drug Screening in Gastric Adenocarcinoma
Curr. Med. Chem. 32, 2409 (2025); https://doi.org/10.2174/0109298673288604240408065715
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  • Article Type:     Research Article
Keyword(s): CLDN18; drug; machine learning; niraparib; prognosis; stomach cancer

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